Ridgeline plot
Contents
7. Ridgeline plot¶
7.1. 概要¶
7.2. Plotlyによる作図方法¶
7.3. MADB Labを用いた作図例¶
7.3.1. 下準備¶
import pandas as pd
import plotly.express as px
import warnings
warnings.filterwarnings('ignore')
# 前処理の結果，以下に分析対象ファイルが格納されていることを想定
PATH_DATA = '../../data/preprocess/out/magazines.csv'
# Jupyter Book用のPlotlyのrenderer
RENDERER = 'plotly_mimetype+notebook'
def show_fig(fig):
"""Jupyter Bookでも表示可能なようRendererを指定"""
fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
fig.show(renderer=RENDERER)
def add_years_to_df(df, unit_years=10):
"""unit_years単位で区切ったyears列を追加"""
df_new = df.copy()
df_new['years'] = \
pd.to_datetime(df['datePublished']).dt.year \
// unit_years * unit_years
df_new['years'] = df_new['years'].astype(str)
return df_new
df = pd.read_csv(PATH_DATA)
7.3.2. 年代別・作品別の合計連載週数¶
df = add_years_to_df(df, unit_years=10)
df_plot = \
df.value_counts(['years', 'cname']).reset_index(name='weeks')
# 年代順にソート
df_plot = df_plot.sort_values(
'years', ascending=False, ignore_index=True)
fig = px.violin(
df_plot, x='weeks', y='years', orientation='h')
fig.update_traces(
side='positive', scalemode='count', width=4)
show_fig(fig)
fig = px.violin(
df_plot, x='weeks', y='years', orientation='h')
fig.update_traces(
side='positive', scalemode='count', width=4)
fig.update_xaxes(range=[0, 200])
show_fig(fig)
7.3.3. 年代別・作者別の合計連載週数¶
df = add_years_to_df(df, unit_years=10)
df_plot = \
df.value_counts(['years', 'creator']).reset_index(name='weeks')
# 年代順にソート
df_plot = df_plot.sort_values(
'years', ascending=False, ignore_index=True)
fig = px.violin(
df_plot, x='weeks', y='years', orientation='h')
fig.update_traces(
side='positive', scalemode='count', width=4)
show_fig(fig)
fig = px.violin(
df_plot, x='weeks', y='years', orientation='h')
fig.update_traces(
side='positive', scalemode='count', width=4)
fig.update_xaxes(range=[0, 200])
show_fig(fig)